The present invention relates to graphical processing methods and systems. More particularly, graphical methods and related systems relating to the segmenting of an organ, such as a heart, from medical imaging information are disclosed.
Cardiac Computer Tomography (CT) is a fast, non-invasive, sub-millimeter resolution medical imaging tool. However, three-dimensional (3D) visualization requires human interaction to prepare the data, and to remove structures that do not belong to the heart (such as the lungs, liver, ribs, etc.). To assist cardiologists, techniques to segment the chambers of the heart for diagnostic purposes have been developed, but these techniques have not been used to visualize the heart surface. For these methods to be suitable across a wide array of conditions and applications, they must be fast enough for casual use, while robust enough to handle different kinds of patients and pathologies. The methods should also permit user-interaction at any point in the process, preserving the flexibility which may be required in daily use. Segmenting the heart allows it to be easily visualized within a volume of data and enables the total volume of the heart to be measured. Additionally, segmenting the entire heart allows the coronary vessels on the surface of the heart to be easily visualized.
A very large body of work exists related to the segmentation of the left ventricle of the heart in two-dimensional (2D) images, but this work is not relevant for purposes of the instant disclosure. In 3D, the published work on cardiac segmentation has been model-based. A great deal of work has gone into 3D, model-based segmentation and analysis of the left ventricle, and in some cases for both the left and right ventricles of the heart. For an overview of the art, refer, for example, to Alejandro F. Frangi, Wiro J. Niessen, and Max A. Viergever, “Three-dimensional Modeling for Functional Analysis of Cardiac Images: A Review,” in IEEE Trans. on Medical Imaging, 20(1):2-25, January 2001. Examples of more recent work may be found, for example, in “Automatic Construction of Multiple-object Three-dimensional Statistical Shape Models: Application to Cardiac Modeling,” IEEE Trans. on Medical Imaging, 21(9):1151-1166, September 2002, by Alejandro F. Frangi, Daniel Rueckert, Julia Schnabel, and Wiro J. Niessen; “3-D Active Appearance Models: Segmentation of Cardiac MR and Ultrasound Images,” IEEE Trans. on Medical Imaging, 21(9):1167-1178, September 2002, by Steven C. Mitchell, Johan G. Bosch, Johan H. C. Reiber Boudewijn P.F. Lelieveldt, Rob J. van der Geest, and Milan Sonka; and “Deformable Biomechanical Models: Application to 4D Cardiac Image Analysis,” Medical Image Analysis, 7(4):475-488, December 2003, by M. Sermesant, C. Forest, X. Pennec, H. Delingette, and N. Ayache.
This prior art isolates and defines one or two ventricles of the heart, but does not isolate the whole heart. Some more recent work attempts to segment all four chambers of the heart using a model-based approach. Examples of such work are presented by, for example, Ting Chen, Dimitri Metaxas, and Leon Axel, “3D Cardiac Anatomy Reconstruction Using High Resolution CT Data,” in MICCAI, pages 411-418 (2004); Juha Koikkalainen, Mika Pollari, Jyrki Lotjonen, Sari Kivisto, and Kirsi Lauerma, “Segmentation of Cardiac Structures Simultaneously from Short- and Long-axis MR Images,” in MCCAI, pages 427-434 (2004); and Marcin Wierzbicki, Maria Drangova, Gerard Guiraudon, and Terry Peters, “Mapping Template Heart Models to Patient Data Using Image Registration,” in MCCAI, pages 671-478 (2004). The segmentation provided by these prior art methods is generally slow because of the need to simulate mechanical deformations of a graphical model in 3D. These methods are also inefficient at isolating the whole heart because the focus is on segmenting the chambers of the heart and not the surface of the heart.
It is therefore desirable to provide methods and related systems that are capable of isolating the whole heart efficiently in 3D from volumetric data.